Flexible Standardization: Making Interoperability Accessible to Agencies with Limited Resources Nadine Schuurman ABSTRACT: Semantic standardization is an integral part of sharing data for GIS and spatial analysis. It is part of a broader rubric of interoperability or the ability to share geographic information across multiple platforms and contexts. GIScience researchers have made considerable progress towards understanding and addressing the multiple challenges involved in achieving interoperability. For local government agencies interested in sharing spatial data, however, current interoperability approaches based on object-oriented data models represent idealistic solutions to problems of semantic hetero- geneity that often exceed the level of sophistication and funding available. They are waiting for the market to decide how interoperability should be resolved. In order to assist in this transition, this paper presents a rule-based Visual Basic application to standardize the semantics of simple spatial entities using several classification systems. We use the example of well-log data, and argue that this approach enables agencies to share and structure data effectively in an interim period during which market and research standards for semantic interoperability are being determined. It contributes to a geospatial data infrastructure, while allowing agencies to share spatial data in a manner consistent with their level of expertise and existing data structures. KEYWORDS: Standardization, interoperability, semantic heterogeneity, classification, Visual Basic Nadine Schuurman is Assistant Professor, Department of Geography, Simon Fraser University, 8888 University Drive, Burnaby BC, Canada V5A 1S6. Tel: (604) 291-3320. E-mail: <schuurman@sfu.ca>. URL: <www.sfu.ca/gis/schuurman>. Cartography and Geographic Information Science, Vol. 29, No. 4, 2002, pp. 343-353 Introduction I nteroperability and standardization of spatial data are recognized as fundamentally impor- tant goals by international, national, and provincial data and mapping agencies (Masser 1999) (Salgé 1997; Salgé 1999; Albrecht 1999a). GIS researchers and computer scientists have conducted considerable basic research in this area over the past decade and have contributed to a more sophisticated understanding of schematic, syntactic, and semantic aspects of interoperabil- ity (Stock and Pullar 1999; Vckovski 1999; Bishr 1998). These three aspects of interoperability dominate logic and software-related research─as distinct from networking and hardware related issues. For the purposes of this paper, Bishr’s (1997; 1998) distinctions between schematic, syn- tactic, and semantic interoperability are used. Schematic heterogeneity (or differences) arise from using a divergent classification scheme. For example, spatial entities (such as a wheat field) described in one database as objects might be described as attributes (type of crop) in a different schema. Schematic differences can also arise from diverse methods for aggregating data or from databases with different attributes representing the same entity, missing attributes, or entities with implicit attributes. Syntactic heterogeneity, on the other hand, arises from the use of different data models and varied ways of addressing entities and attributes (Bittner and Edwards 2001; Brodaric and Hastings 2002). Syntactic differences are more easily reconciled than semantic heteroge- neity. This is because database formats can be changed without affecting the integral meaning or interpretation of data. Semantic heterogeneity is so difficult to reconcile because it arises from differences in language and meaning (Vckovski et al. 1999). Facts and spatial entities have dif- ferent descriptions, depending on the discipline describing it and their context (Hill 1997). In GIS, the close relationship between language and data must be reconciled by interoperability. Semantic interoperability requires reconciling the meaning of data terms that vary between domains. Research on syntactic, schematic, and semantic interoperability is fundamental to GIScience, but it does not directly address a range of social and institutional problems associated with implemen- tation. Foremost is the reluctance of government agencies and businesses to embrace innovative, but unproven, interoperability solutions. The test of time and widespread adoption is the yardstick by which such agencies judge interoperability. Off-the-shelf software is not yet designed to handle semantic heterogeneity among databases. When joining data tables from different sources, for example, there is no way to identify fields that refer to the same entity